Advertising translators as agents of multicultural marketing: a case-study-based approach
Bibliographic record
Abstract
In the last decades, economic, social, and technological factors have led to an increase of multilingual advertising operations. Although this issue has been addressed theoretically and with corpus-based comparative studies, concrete manifestations of this reality on the professional market are still to be documented. We therefore conducted two case studies following professional translators in advertising agencies. To do so, we set up a research methodology comprising non-participant direct observations and semi-structured interviews to collect data on the duties, responsibilities, work environment, and professional relationships of the advertising translator. Our case studies demonstrate that advertising adaptation assignments go far beyond linguistic preoccupations, and that the translator acts as a multitasking cultural agent. In our first case study, the translator is involved in the entire process of producing a TV spot, from the casting to collaborating with the editor (as opposed to simply translating the on-screen text). In the second case study, after adapting corporate publications for social media, the translator is allowed to create French responses in the name of the brand, since he knows the client and his product as well as the creative team that created the original English messages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".